WEEK 1- INTRO AND

WHAT IS A

STATISTICAL MODEL

SDS 290

Scott LaCombe

Todays Plan

◦ Introductions

◦ Course overview

◦ CITI training set up

A Little About Me

J O INT G O V / S D S P O S I T I O N

G R A D U AT E F R O M U N I V E R S I TY O F I O WA

O R I G I N A L LY F R O M KA N S A S C I TY, M O

I F O C U S O N STAT E P O L I T I C S A N D N E T W O R K S O F

P U B L I C P O L I C I E S

Teaching Assistant: Gollum LaCombe

Introductions (in small groups)

◦ Name

◦ Major

◦ Year at Smith

◦ Most recent show/movie you’ve been obsessed with

Goals of this course

◦ Understand fundamentals of experimentation and observational research

◦ Learn about how to design and implement survey experiments

◦ Implementing ANOVAs and similar models

◦ Use software and data to answer real world questions about the world around us

Quick note on R and Stats Background

◦ Assumption- you’ve taken an introductory stats class

◦ Demonstrate familiarity with descriptive statistics, normal/t distribution, hypothesis testing,

p-values, and confidence intervals

◦ We will be using R extensively in this class

◦ Will start slow, but quickly build

◦ If you are unfamiliar with R, I strongly suggest working through first 4 chapters of

ModernDive (see syllabus)

◦ Talk with me, go to stats Tas

◦ SDS 100

Tips for Learning this semester

◦ Office hours:

◦ Mondays: 2-3, Wednesdays 11-12, Thursdays 4:15-5:15

◦ Complete readings before class

◦ Use office hours and tutors

◦ Post on slack

◦ If you have a question, someone else probably does too

◦ Also counts toward participation

◦ Keep me in the loop if you are struggling inside/outside class

◦ Much easier to give extensions before due date than after

Slack Chanel & Moodle

A note on course Delivery and Participation

◦ Will record lectures, no remote option

◦ Welcome to “zoom-in” classmate

◦ Participation and attendance contribute to course participation grade

◦ If you can’t make it to class, email me and post something on slack.

◦ In person R labs are critical to your learning

◦ I’m trying to be as flexible as possible, extending the same to you

◦ In person attendance expected, but if you are feeling sick, close exposure, watch recording and get notes from a friend

Syllabus

Walkthrough

Basic Structure of Course

◦ Lecture with periodic group discussion/prompts

◦ Weekly(ish) homework assignments, due Fridays at 11:59 PM

◦ Periodic R workshops to build programming skills

◦ 2 exams

◦ 2 mini projects- will talk about more next week

◦ 1 mini project solo

◦ 1 in groups of 3

◦ Design and implement survey

QUESTIONS?

Before we get started with content…

◦ By tomorrow- Fill out introductory survey

◦ For next Friday

◦ CITI training

◦ Intro to R lab

◦ Don’t put off! Citi training takes a bit of time

WHAT IS A

STATISTICAL MODEL?

Problem we face today- so much data!

Our goal

◦ With so much info, how to separate out the signal from the noise?

◦ Our approach- Modeling

◦ Simplification of complex processes to use data to better understand the

world around us

◦ All models are wrong, some are better than others

◦ World is complex, shouldn’t forget that

◦ Uncertainty is central

Goal of modeling

◦ Prediction

◦ Classification

◦ Evaluating a treatment

◦ Testing a theory

◦ Summarizing a pattern

◦ Improving a process

◦ Making a decision

What should our goal be?- small groups

Model basics

◦ Y=model+error

◦ Y

◦ Dependent variable, response variable, outcome

◦ Thing we are trying to explain/model

◦ Model

◦ Explanatory variables, independent variables

◦ Error

◦ Residuals, difference between predicted and observed

Other important terms

◦ Sample vs Population

◦ Statistic vs Parameter

◦ Inference

◦ Parameter estimate

◦ Causal inference

◦ Role of experimentation vs observation

◦ Covariates

Data

◦ Cases

◦ Unit of analysis

◦ Variables

◦ Types of variables

◦ Quantitative/continuous

◦ Categorical

◦ Ordinal vs nominal

◦ binary

What types of variables are these?

◦ Race

◦ Education level

◦ Income (in dollars)

◦ left-handed/non-left handed

◦ Voter turnout

◦ Letter grade in a course

Modeling Process- 4 steps

◦ Choose a form for the model

◦ Fit the model

◦ Assess the Model

◦ Address research question

◦ Theory comes first

Our plan

◦ Anova- Analysis of Variance

◦ Response variable Quantitative

◦ Explanatory variable typically categorical

◦ Fundamentals of experimentation

◦ Later in semester- Causal inference with observational data